Social media capital: Conceptualizing the nature, acquisition, and expenditure of social media-based organizational resources
Bibliographic record
Abstract
The near-universal organizational participation in social media is predicated on the belief there are some tangible or intangible new resources to be had through tweeting, pinning, posting, friending, and sharing. We argue the linchpin of any payoff from engagement in social media is a special form of social capital we refer to as social media capital, and offer a conceptual framework for understanding its nature, acquisition, and expenditure. This paper contributes to existing literature by elaborating a new type of organizational resource and then synthesizing and extending research on the processes through which organizations can translate social media efforts into meaningful organizational outcomes. Understanding this causal chain is critical not only for measuring the return on investment from social media use but also for developing accounting information systems that are both adaptable to social resources and better able to exploit the data analytic and forecasting capabilities of real-time social media data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".